In the present article, the flow field and passive gaseous pollutant dispersion in a naturally cross-ventilated isolated single-zone model building have been investigated. The large eddy simulation (LES) approach has been applied together with three different sub-grid scale (SGS) models, namely, wall-adapting local eddy-viscosity (WALE), dynamic Smagorinsky-Lilly model (DSLM), and standard Smagorinsky-Lilly model (SSLM). The flow and passive scalar concentration fields have been compared with available experimental data. It is demonstrated that the three SGS models predict similar flow field. Also, it can be observed that the mean concentration field results obtained from the WALE sub-grid scale (SGS) model are in better agreement with the experimental data than those of DSLM and SSLM by about 8% and 5%, respectively. Moreover, the CPU time required for the computations by the WALE and SSLM models was almost 20% less than that of DSLM, making WALE a more suitable SGS model than the other two models for the prediction of flow and concentration fields in an isolated building. Furthermore, contributions of convective flux and turbulent diffusion flux to the pollutant transportation have been studied. It has been shown that although the convective flux is the main mechanism, the two fluxes have a significant influence on pollutant transportation and distribution.

In this paper, the effects of the sub-grid scale (SGS) turbulent Schmidt number, Sc_SGS, on the large eddy simulation of dispersion on and around an isolated cubical model building with a flush vent located on its roof are examined. Constant and dynamic Sc_SGS approaches for SGS turbulent mass flux modeling are employed. Simulation results are compared with the available wind tunnel measurements. Furthermore, the influence of the grid resolution on the accuracy of results predicted by the dynamic Sc_SGS approach is investigated. Detailed statistical analysis of Sc_SGS demonstrates that the dynamically computed Sc_SGS at different locations varies by a factor of almost 5 and a considerable deviation of Sc_SGS from its common values of 0.5 and 0.7 occurs. Particularly, in the vicinity of the building where the concentration gradients are noticeable, Sc_SGS has a larger variation. Also, the probability of occurrence of 0.2 < Sc_SGS <1.5 is more than 90 percent and the Sc_SGS mean values are nearly around 0.8 to 1 with a maximum variance of 0.2. In addition, by refining the grid, the differences between the predictions of constant and dynamic Sc_SGS approaches decrease. This is due to the reduction of sub-grid scales contribution to turbulent dispersion. It is confirmed that dynamic Sc_SGS approach is a practical alternative to the constant Sc_SGS approach, effectively eliminating a user-defined model coefficient.

Prediction accuracy of various non-linear eddy viscosity turbulence models for simulating flow and pollutant dispersion on and around an isolated cubical model building with a rooftop vent within the neutral turbulent boundary layer was investigated. For this purpose, three types of quadratic along with three cubic non-linear models were employed and simulation results were compared with the available wind tunnel measurements and linear revised k-ε models. They were different from the preceding simulations which have only concentrated on the wind flow field around buildings. Detailed analysis of dispersion mechanisms based on convective and turbulent diffusion mass fluxes indicated that concentration distributions predicted by non-linear models at the sidewall improved significantly relative to the traditional standard k-ε and linear revised k-ε models which was due to larger lateral turbulent diffusion. Moreover, thorough analysis of these fluxes underlined the prominent capability of non-linear models in capturing the anisotropy of turbulence and verified the importance of recirculation regions in the pollutant dispersion around a model building. Among the various non-linear models under study, cubic models of Craft et al. and Ehrhard and Moussiopoulos provided the best performance as compared with the other numerical and experimental data.